This has significantly helped devs and made sure that requirements are very clear.
Honestly, with the first step, it seems the PMs are already halfway there to implementation of the feature so I wonder if in the future they'll just do everything themselves and a few devs will be around as SDETs rather than full blown implementers.
I am a very AI-forward person, but hallucinations are becoming more pernicious than ever even as they get less frequent, especially if the code actually works. A human absolutely has to guide these processes at a macro level for sustainability for SaaS as it evolves with business needs.
Maybe for one and done systems with no maintenance/no updates/no security patches you can reduce humans to SDETs, but systems like that are more the exception than the norm.
At least with concurrent and distributed systems stuff (which is really all I know nowadays), it is great at getting a prototype, but the code is generally mediocre-at-best and pretty sub-optimal. I don't know if it's because it is trained on a lot of mediocre and/or buggy code but for concurrency-heavy stuff I've been having to rewrite a lot of it myself.
I think that AI is great for getting a rough POC, and admittedly often a rough POC is good enough for a project (and a lot of projects never get beyond a rough POC), but I think software engineers will be needed for stuff that needs to be more polished.
I'm pretty sure it still saves me time, and if nothing else it's an excuse to write TLA+, and that's fun.
I had this same discussion at work the other day. I had an 80k line generated project dropped on my plate. It doesn’t use anything built into the web framework or orm. It’s a maintenance nightmare.
Example: I got Claude to generate a language server for TLA+ so I could have nice integration with Neovim. It took like 45 minutes of arguing with Claude and then it worked fine. This is incredibly low-stakes stuff: realistically the worst case scenario is that the text in the file gets screwed up, and I'm somewhat protected by Git if that happens.
That said, I am a little concerned how cavalier people have been deploying AI code everywhere. I don't want pacemaker firmware to be written by some intern in an afternoon with Claude.
Even still, other professions interact with the real social world which is not necessarily the case with programming. A lawyer will always be needed because judgments are and must be made by humans only. Software on the other hand can be built and tested in its own loop, especially now with human readable specifications. For example, I wanted to build an app and told Claude and it planned out the features, which I reviewed and accepted, then it built, wrote tests, used MCPs including the browser for interacting with the UI and taking screenshots of it, finding any bugs and regressions, and so on until an hour later it came back with the full app. Such a loop is not possible in other professions.
It's when you have to iterate to handle changing business needs, scale issues, and integrate with other systems where the entropy becomes a scary concern over a long enough timeline.
And it's not just "checking" - it's wholesale rejections of code, reframing prompts to target specific classes or approaches, etc... I don't think you will take the human out planning any time soon.
Honestly, I believe lower court judges will be the first job in the legal industry to become fully automated.
PMs turning their brain off and letting the LLMs extrapolate from quick and dirty bashing of text into a template (or, PMs throwing customer feedback at a slackbot to generate a jira ticket form it) can be better than PMs doing nothing but passing ill-defined reqs directly into the ticket, but that's a low bar. And it doesn't by itself solve the problems of the details that got generated for this ticket subtly conflicting with the details that got generated for (and implemented) in a different ticket 8 months ago.
The bottleneck is understanding, never "code generation."
Below is an an axiom which has served me well over the years. Perhaps it will for you as well.
When making software, remember that it is a snapshot of
your understanding of the problem. It states to all,
including your future-self, your approach, clarity, and
appropriateness of the solution for the problem at hand.
Choose your statements wisely.I'm guessing they've tried (or been induced to try by upper management), but given up because they don't know how to debug any problems that arise due to the LLM working itself into a corner.
Coding-agent LLMs act a lot like junior devs. And junior devs are: eager to write code before gathering requirements; often reaching for dumb brute-force solutions that require more work from them and are more error-prone, rather than embracing laziness/automation; getting confused and then "spinning their wheels" trying things that clearly won't work instead of asking for help; not recognizing when they've created an X-Y problem, and have then solved for their Y but not actually solved for the original problem X; etc.
The way you compensate for those inexperience-driven flaws in junior devs' approach, is to have them paired with, or fast-iteration-code-reviewed by, senior devs.
Insofar as a PM has development experience, it's usually only to the level of being a "junior dev" themselves. But to compensate for LLMs-as-junior-devs, they really need senior-dev levels of experience.
The good PMs know all of this, and so they're generally wary to take responsibility for driving the actual coding-agent development process on all but the most trivial change requests. A large part of a PM's job is understanding task assignment / delegation based on comparative advantage; and from their perspective, it's obvious that wielding LLMs in solution-space (as opposed to problem-space, as they do) is something still best left to the engineers trained to navigate solution-space.
Super glad to have gotten out when I did...
Halfway there feels way overblown, and only seems to further devalue to work that devs do. Having clearly written requirements would be fantastic, and even as someone less pro AI even I can see great utility for it here, but its not halfway there to implementation. Not even 25% in all honesty, since edge cases and unforeseen consequences can cause changes to the spec midway through development.
Judging by every PM I’ve worked with, 0% chance of this happening. Much sooner would see SWEs making PMs redundant than the other way around. Unless of course you want a system that falls apart like a house of cards as soon as you get a single user for your vaporware.
And then someone copy pastes it into Claude and now those inaccuracies become part of the code and tests.
It's the equivalent of writer's block and is why a common advice given to writers is to put anything they can onto the page then edit it later.
The PM has historically often not had a detailed enough mental model of the implementation to spot the hard parts in advance or a detailed enough mental model of the customer desires to know if it's gonna be the right thing or not.
Those are the things that killed waterfall.
You can use LLM tools to help you improve both those areas. Synthesizing large amounts of text and looking for inconsistencies.
But the 80th-percentile-or-lower person who was already not working hard to try to get ahead of those things still isn't going to work any harder than the next person and so won't gain much of a real edge.
Normally waterfall works where the scope is extremely-well defined and articulated in design plans. Which shortens dev time because prior to AI code was mostly deterministic. Here we have to do waterfall level of documentation while iterating on a non-deterministic solution (code gen) to non-deterministic requirements (per usual).
It's bonkers.
I still think the technology is cool though.
And to answer the questioner.. Have you worked with a PM? Most of the ones I've worked with try to be simultaneously in charge yet not responsible for anything. Validating something implies skill and responsibility.
Nobody "deserves" anything. They do have the jobs though. Thinking that the world isn't full of people doing what they need to do to get by who don't give a shit about fitting a fantasy ideal is wild.
We see it with code too right? It’s harder to review code than to write it.
On top of that the LLM can work so fast that the amount of things that need validating grows!
This is where humans get lazy and the problems come in IMO. Whether its a PM not validating their ticket, or a dev doing a bad code review.
Add on to that that the incentives currently are to move fast and trust the AI.
It becomes clear to me that a lot of that review work either won’t be done at all, or won’t be nearly thorough enough.
Reviewing code is harder than reviewing text because code does something and has interdependencies and therefore must be correct in its function, do not mix the two. This is like saying an editor reviewing an article or novel is harder than actually writing the novel which is blatantly incorrect.
Hahahahahaha. Sorry, I couldn't help myself; this reads like satire. The answer is "real life experience says otherwise".
I feel compelled to point out to you that this is a completely unsustainable, unsupportable, unsubstantiable claim. You have met ~0% of PMs, and of the ones you've met maybe you've experienced a non-zero percentage of their work, but statistically that's also very unlikely.
If you think you can say what most PMs do or what PMs are likely to do, then, I'm sorry, but you are not even thinking like an engineer. You're thinking, actually, a lot more like a PM to many of us.
> just like good devs
I'm so sorry, my sides just can't handle the starry-eyed nature of these takes. This is just too much for me.
To many of us this reads like you've never met people before. But who knows, maybe you live in Lake Wobegon, where all the women are strong, all the men are good-looking, and all the children are above average! If so then we're jealous, but you still should be more careful about how unrigorous your mental model is because it will make you a worse engineer.
Experience with different PMs and developers aside, the older you get in the profession the more you will hopefully realize that none of your quality effort fantasy matters. Sales happen and money rolls in independently of whether you think the PMs or the people who call themselves engineers do a "good job". Businesses thrive on sales and marketing, not engineering.
As to your latter point, not sure why you think I think business doesn't continue on even with bad employees, of course it does and I didn't say otherwise. But that does not mean they're doing a good job, those two are orthogonal concepts.
And I'm not sure how we even got to this, the original point was that I personally as a dev can physically see PM productivity increasing with AI, even as other devs in this thread seem not to. For a competent PM, a tool that automates a detailed first draft fundamentally changes the psychology of ticket creation. If your argument is just "bad PMs will still be bad," then sure, I agree, but that doesn't really engage with how the tooling changes the workflow for everyone else.
Uh. We're not talking about knowing what good is, which is completely irrelevant to anything in this thread. You made a claim without qualification about what it is more likely for PMs to do. I can't tell if you've lost the chain or are engaging in some kind of motte and bailey fallacy. Either way it's a bad sign for this conversation.
I'm going to summarize the threads so far. I hope it highlights why what you've said sounds so silly:
Someone: "I see X failing to do Y."
You: "X definitely do Y. Why would you think that X aren't doing Y? Doing Y is the obvious thing for X to do."
Someone: "I literally am seeing it happen right now."
You: "Well then those X are bad."
Someone: "Yeah, no shit. They just said as much."
You: "But most X would do Y."
Someone: "In my experience that is false."
Someone else: "Mine too."
Someone else: "Mine as well."
Someone else: "Same."
You: "The bad ones shouldn't have their jobs."
Someone: "They do though."
You: "But we can tell which ones are the bad ones."
Someone: "Bartender, another drink please."
If your technology relies on humans using it in ways that go against the ways they are inclined to use them, then that is an issue with the technology.
Are advanced calculators bad because a student could use the CAS to ace calculus homework, exams or the SAT without actually learning the material?
Is copy/paste bad because a person could use it to copy/paste code from one place to another without noticing some of the areas they need to update in the new location, adding bugs and missing a chance to learn some more subtleties of the system?
Is Git bad because a manager could use it to just measure performance by number of lines of code committed instead of doing more work to actually understand everyone's performance?
Many tools can be used lazily in ways that will directly work against a long term goal of improving knowledge and productivity.
ok, so for some of the jobs we're doing plausible sounding goo is just fine. and that's kinda sad. but the 'just playing around' case is fine for PSG, this isn't a serious effort but just seeing how things might work out without much effort.
taking the remainder, where understanding and intent are important, the role of the ai is produce PSG, but the intentional person now goes through everything and plucks out all the nonsense. this may take more or less time than simply writing it, but we should understand this is resulting in less real engagement by the ultimate author. where this is actually interesting is a parallel to Burrough's cutup method - where source text and audio were randomly scrambled and sometimes really clever and novel stuff pops out.
but to say the current model of vibe coding has much to offer in the second case is really quite unclear. to the extent to which coding is the production of boilerplate is really a problem with APIs and abstraction design. if we can get LLMs to mitigate some of that I the short term without causing too much distraction, that's fine, but we should really be using that to inform the solution to the fundamental problem.
so for me what's missing in your model is how LLMs are supposed to be used 'properly'. I don't think laziness is really the right cut here, make-work is make-work, and there's plenty of real work to be done. but in what sense does LLM usage for code actually improve our understanding of these systems and get us more agency?
> People who use AI because they are trying to avoid doing work fall into a completely different category than people using AI as a force multiplier and for skills/capabilities enhancements / quality improvement.
This statement is absolutely true. There are ways to use LLM tools to significantly improve the quality of your work instead of to avoid doing hard work. (And the result can easily become something that requires more hard thought, not less.)
Some that I frequently enjoy that are usable even if you don't want the machine to generate your actual code at all: * consistency-check passes asking it to look for issues or edge cases * evaluation of test coverage to suggest any missed tests or proposed new ones * evaluation of feasibility of different refactoring approaches (chasing down dependencies and call trees much more faster than I would be able to do by hand, etc)
> to the extent to which coding is the production of boilerplate is really a problem with APIs and abstraction design. if we can get LLMs to mitigate some of that I the short term without causing too much distraction, that's fine, but we should really be using that to inform the solution to the fundamental problem.
I generally would disagree with this, though. I don't think there's solely a problem with abstraction design, I think the inherent complexity of many systems in the business world is very high (though obviously different implementations make it different levels of painful). If that's a problem, it's a people/social one, not a technology problem.
In my future we lean into the fact that people want features, they want complexity, for many things - everybody's ideal just-for-them workflow/tooling would look slightly different than the next person's - and use these tools to build things that do more, not less. Like the evolution of spellcheck from something you manually ran, to something that constantly ran, to something that can autocorrect generaly-usefully when typing on a touchscreen.
Let's get back to finding more features/customization to delight users with.
This isn’t actually an argument for or against anything, I don’t know why people say this. It is entirely possible that people are using this brand new, historically unprecedented tool wrong.
Cars have been a huge success in spite of requiring people to learn a bunch of new things use them.
The classic "you're holding it wrong" was about the iPhone 4: sure, people could learn to hold the iPhone in such a way that they didn't block the particular parts of the antenna that were (supposedly) the problem. But "holding an iPhone" is a fairly natural thing to do, and if the way that people are going to do it naturally doesn't allow its antenna to connect properly, then that's a technology problem, not a human problem.
If the selling point for AI is "you can just talk to it, and it will do stuff for you!" (which may or may not be yours, personally, but it is for a lot of people), then you have to be able to acknowledge that "describing a problem or desire using natural language" is something that humans already do naturally. Thus, if they have to learn to describe their problem in very specific ways in order to get the AI to do what they want, and most people are not doing that, then that's a failure of the technology.
For the specific case at hand, what's being described is similar to the problem of self-driving cars: you're selling the benefit as being the AI taking a lot of the work off your shoulders; all you have to do is constantly check its work just in case it makes a mistake. Which is something that we already know, empirically and with lots and lots of data, that humans are bad at.
Once again, it's a technology issue. Not a human issue.
Cars can take you from place to place much faster than a horse can, all you have to do is learn to drive and constantly keep your hand on the wheel.
Part of using a technology is, well, learning how to use it. It's not the technology's fault that humans are lazy or not able to pay attention and crash.
Some people are lazy, plain and simple. If they want to blindly accept what the LLM tells them without critical analysis and review then that's on them.
Just lol. Is this what you guys mean by productivity boost?
Comical. LLM’s aren’t all that great - it’s more that most orgs are horribly inefficient. Like it’s amazing how bad they are.
That’s why Elon succeeded with spacex - he saw how horrible inefficient the industry was. And used that thinking to take a gamble and it’s paid off.
Considering that that’s been a running complaint for like 50 years, it doesn’t seem like project management is going to get better on its own at this point. So, yes, an LLM does represent a productivity boost in that area.
When the org is misaligned, mismanaged, has poor customer feedback loops, bad product market fit, too much bureaucracy, etc etc no amount of AI slop is going to make a meaningful impact on its bottom line. In fact, it will likely do the opposite through combination of exponentially increasing complexity, combined with worker force deskilling, layoffs, and rising token prices. Real bottleneck is and always has been communication & alignment.
It might make the employees _happier_ in the interim though, which, I believe, is what we're predominantly seeing during this AI mania. People fed up with the bullshit jobs of rewriting the same service for the 5th time in 2 years or creating TPS reports weekly just for their manager to throw them directly in the trash are absolutely giddy that they no longer have to do this manually. I think we need to question the economic value of these jobs in the first place, though.
I've worked at big tech prior to LLMs becoming a thing, and consistently saw projects of 20-50 people carried by 2-3 individuals that actually understood what needed to be done. I don't think this ratio will be any better with genAI, and I also don't think that tokenmaxxing has any meaningful correlation with impact. Bullshit jobs (and questionable personal projects) just get done faster now. Yay, I guess.
In the long run these highly inefficient firms are going to get destroyed by people who have a vision and can do what 100+ firms are doing and package it together as one solution that is far superior on dimensions that matter to firms.
At least when the PM still wrote it you could outright tell it was bullshit and made no sense. Now that is just obfuscated.
The truth of the matter is that software starts as basic CRUD and then given time and users evolves into its own special snowflake. Every single system given enough time and users will become a “complex system”.
Yes please, I've seen the vibecoded slop PMs put out every day because software engineering is simply not a skill they have, and I'd love to make a LOT of money fixing their crap once it dies in production <3
I can tell you right now most pm’s are absolutely useless and glorified project managers who don’t know how to think and get in the way - and don’t know how to enable engineers to be more productive.
This was substantially predicted by Fred Brooks in 1986 in the classic No Silver Bullets [1] essay under the sections "Expert Systems" and "Automatic Programming".
In it, he lays out the core features of vibe coding and exactly the experience we are having now with it: Initial success in a few carefully chosen domains and then a reasonable but not ground breaking increase in productivity as it expands outside of those domains.
[1] https://worrydream.com/refs/Brooks_1986_-_No_Silver_Bullet.p...
The LLMs turn out fully formed clones of stuff for which there exists copious amounts of code openly searchable on the web doing the exact same thing.
LLMs require developer-like specification, task/subtask breakdown and detail where such example code already exists.
As a professional prior to LLMs, how many problems that you work on have many existing free solutions but you neglected to use that code and decided to spend days doing it yourself?
I’ve often reimplemented things at work that exist elsewhere. If I could just copy & paste whole solutions from GitHub and change the branding/naming slightly, I could make curl in an afternoon.
I can only think of hobby projects, like writing yet another emulator, expression parser or media processor in a new language I'm trying to master.
In a professional setting, you would always diligently explore libraries and only implement your own if there is no suitable alternative.
Only when the existing free solutions are licensed with something like GPL. Now I can just say, write me a C webserver library similar to mongoose and I get the functionality without the license burden.
And you now own full responsibility for maintenance.
Also I was joking, I'd never do that; feels gross. But I suppose it is a legitimate "productive" use of AI.
I read how that’ll read to VCs coming from Altman and Musk and, ow, the entire stock market just made sense for a second.
I need a python script that
1) reads /etc/hosts
2) find values of specific configured hosts (read from a .conf which) eg server1, localhost, etc
3) it'll assign a name to those configs eg if the .conf has
[Env1]
192.168.0.1 production-read
192.168.0.2 production-write
192.168.0.27 amqp
[Env2]
192.168.0.101 production-read
192.168.0.201 production-write
192.168.1.127 amqp
Basically format:
[CONFIG_NAME]
<ip> <hostname>
Like an usual hosts file
4) And each of those will be stored in memory
5) if in /etc/hosts it matches one of those, it sets the "current env" as the configname
5) It'll create an icon on the top-right of ubuntu 22 default gnome with
6) that icon could be the text of the current config name or if nothing matches, "custom" text would show
7) When the user clicks the "tray"/appindicator(or whatever gnome is calling them) it'll list the config names in a simple gtk/gnome
8) When the user clicks one config, we create a backup of /etc/hosts in ~/.config/backups/ named hosts-%UNIX_TIMESTAMP%
9) we then apply it to hosts file (find only the line with the hostnames to change and modify only those)
This is one of the reasons I like the OpenBSD and suckless projects. There are solutions that are technically correct, but are overengineered.
That's (as shown in my sample prompt) one great thing I've been using LLMs for: making GUIs for arcane Linux-based OS/userland settings that I have no interest in doing "sudo gedit yadda yadda" or learning man pages for. It's been 30+ years, we deserve a better desktop experience.
I've used suckless packages in the past, but it feels to me too close the GNOME/Apple way of giving zero settings and having opinionated defaults whose opinions do not ring well for me. I have zero desire to change my shortcuts/hotkeys to something random devs chose based on their past computer experience, mostly unix-based. Muscle memory > *.
I was pointing out that a simpler solution exists. I prefer simple solutions, because I want to test whatever idea I have in real world situation first before I go for a more complete one. Kinda like doodling before committing to do a sketch (or spend weeks doing a painting).
> It's been 30+ years, we deserve a better desktop experience
That desktop experience would need to be like smalltalk (where it’s trivial to modify the gui). The nice power of Unix is having the userland being actually a userland. Meaning you can design a system for your workflow and let the computer take care of that. Current desktop environment doesn’t allows for that kind of flexibility.
Also it’s the nature of unix that makes such basic utilities possible (and building them with raw xlib or tcl is easier than gtk). Imagine doing the same on macOS or Windows where everything is behind an opaque database where some other process fancies itself as its owner.
I mean, no comment
LLMs just take the same vague or poor requirements and make them look believable until you dig in to them.
You make it sound like writing good requirements is easy.
If it were easy we wouldn't need all these concepts around PMF, product pivots and the like. And even before that was Peter Naur's paper "Programming as Theory Building" [1].
If you truly understand the problem you're solving with software then requirements can be easy. But usually we don't, not right away, and so we have to build up our understanding of the problem first in order to solve it.
Even then, the problem we solve may not have been the problem paying users will have, so you can have "good requirements" and still have a bad business, or even the opposite where you somehow build a working business despite bad requirements, because you hit upon a customer's need quite by mistake.
Nothing about any of this precludes LLMs being helpful, though nothing guarantees LLMs will be helpful either.
[1]: https://cekrem.github.io/posts/programming-as-theory-buildin...
I am certain I didn’t say that. To be a good product owner one needs skill, care and understanding of the business intent. If you know the business intent but lack the skill to express it as a useful requirement then it’s insufficient; if you have the skill but lack understanding or ability to understand the business intent then it’s insufficient; if you have the skill and understand the business intent but you are careless in your work then it’ll be insufficient too. If the problem space is emergent then having all three might not be good enough either.
It’s certainly true that good engineering teams can deeply understand the problem space enough to get to a business outcome without requirement documents.
I just wouldn’t bet that LLMs are going to make any of these realities any better, they might exacerbate those issues.
Yes, that's certainly a fair assessment, especially the more it convinces software developers they can talk to the LLM rather than talking to users.
An LLM will just say, "Sure! Here's the fully implemented code that gets the data and give it to the user. " and be done with it.
> What data should I retrieve, and where should I get it from? Please specify at least: ...
And it then goes on to ask just exactly what is necessary, being all constructive about it.
But the point still stands: in most contexts, the LLM will fill in the blanks with what it deems appropriate like an overconfident intern at best and a bull in a China shop at worst.
It's the wrong thing for important things under the hood (like durability and security requirements) that are not tangible to them.
When we talk about "the" bottleneck being specs it just isnt the case that it's the only thing LLMs do poorly. Theyre really bad at a lot of stuff in the SDLC.
They're also good at providing results which are bad but look ok if you either dont look too closely or dont know what you're looking for.
"Make a facebook clone" is the vague human promise to the end user. The reality is that it leads to so many assumptions which are insurmountable due to the vague interpretation so you have to change your requirements in the end to claim success.
Thus everything turns into a mediocre compromise. There is no exceptional outcome, which is what makes a marketable product. There are just corpses everywhere.
You need something better to both define requirements and implement them than this technology.
Anyone who thought that gap could be shrunk substantially lives in delululand.
Hence why we haven’t seen this explosion of ‘really great’ products come out.
Many will continue to parrot ‘bro but the models changed I swear’. I’m sure they did. But you’re missing the damn point.
"what does X means? how will it work?"
while a programmer will ask, about all cases.
The dudes in Eastern-Wherever not asking what something means is the scary part. You only find out at the end how deeply confused everyone was when making the thing. You can fix it with attention and management, but then only some projects sometimes are profitably outsourced and you still need competency.
Can't good marketing teams, backed up by World Class Product people, sell anything we build, more or less?
</devil's advocate>
In several companies I have seen product managers joining teams and failing to even have minor requirement ready for months during “onboarding” of the PM. And then code being ready but taking months to release because DevOps is busy or QA can’t find time.
The pace of release of software has been disconnected from the coding part for the longest time, and we have been quiet about it.
That's why we write programs in programming languages and not English. Because they are much more efficient at giving precise instructions than natural language.
This is a big HN LLM discussion divide. I am in the same no-specs work background camp, and so the idea that the humans who input that into dev teams are suddenly going to get anything out of an LLM if they directly input the same is laughable. In my career most orgs there has been no product person and we just talked directly to end users.
For that kind of org, it will accelerate some parts of the SWEs job at different multipliers, but all the non-dev work to get there with discussions, discovery, iteration, rework, etc remains.
If the input to your work is a 20 page specification document to accompany multi-paragraph Jira tickets with embedded acceptance criteria / test cases / etc, then yes there is a danger the person creating that input just feed it into an LLM.
Probably why I haven't ended up in any.
https://web.archive.org/web/20161211074810/http://www.commit...
The annoying thing is that giving an LLM vague instructions like "make a Facebook clone" does work... in certain limited cases. Those being mostly the exact things a not-very-creative "ideas person" would think to try first. Which gave the "ideas people" totally the wrong idea about what these things can do.
These same "ideas people" have been contracting human software developers to "make them a Facebook clone" (and other requests of similar quality) for decades now.
And every so often, the result of one of those requests would end up out there on the internet; most recently on Github. (Which is, once there's enough of them laying about, already enough to allow a coding-agent LLM trained on Github sources to spew out a gestalt reconstruction of these attempts. For better or worse.)
But for the most common of these harebrained ideas (both social-media-feed websites and e-commerce marketplace websites fit here), entire frameworks or "engines" have also been developed to make shipping one of these derivative projects as easy as shipping a Wordpress.org site. You don't rewrite the code; you just use the engine.
And so, if you ask an LLM to build you Facebook, it won't build you Facebook from scratch. It'll just pull in one of those frameworks.
And if you're an "ideas person", you'll think the LLM just did something magical. You won't necessarily understand what a library ecosystem even is; you won't realize the LLM didn't just generate all the code that powers the site itself, spitting out something perfectly functional after just a minute.